Optimized Technique for Capacitated Minimum Forest Problem In Wireless Sensor Networks

Shwetal R. Jaiswal
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Abstract

Recent advances in low power radios and sensor technology have enabled the pervasive deployment of sensor networks consisting of sensor nodes that are very small in size and relatively inexpensive. Wireless Sensor Networks (WSNs) have been seen more as a solution to large scale tracking and monitoring applications, because their low data rate, low energy consumption and short range communication presents the great opportunity to instrument and monitor the physical world at unprecedented scale. However realization of WSNs needs to satisfy constraints introduced by factors such as limited power, limited communication bandwidth, limited processing capacity, and small storage capacity. Therefore, the design of efficient technique for optimizing the capabilities of networks is becoming an increasingly critical aspect in networking. This paper addresses constrained optimization problems namely the Capacitated Minimum Forest (CMF) problem. To utilize the critical WSNs resources precisely, the development of algorithms with quality guaranteed solutions in WSNs is needed. We proposed that optimal approximation algorithms achieve highest optimization goal which minimize Cost of network resource consumption.
无线传感器网络中有能力最小森林问题的优化技术
低功率无线电和传感器技术的最新进展使得由尺寸非常小且相对便宜的传感器节点组成的传感器网络得以普遍部署。无线传感器网络(WSNs)已被视为大规模跟踪和监控应用的解决方案,因为其低数据速率,低能耗和短距离通信为前所未有的规模测量和监控物理世界提供了巨大的机会。然而,无线传感器网络的实现需要满足有限的功率、有限的通信带宽、有限的处理能力和较小的存储容量等因素的约束。因此,设计有效的技术来优化网络的功能已成为网络的一个日益重要的方面。本文研究约束优化问题,即容量最小森林(CMF)问题。为了准确地利用关键的无线传感器网络资源,需要开发具有质量保证解的无线传感器网络算法。提出了以网络资源消耗代价最小为最高优化目标的最优逼近算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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